{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PHydro-cover-small.png\">\n",
    "*This is the Jupyter notebook version of the [Python in Hydrology](http://www.greenteapress.com/pythonhydro/pythonhydro.html) by Sat Kumar Tomer.*\n",
    "*Source code is available at [code.google.com](https://code.google.com/archive/p/python-in-hydrology/source).*\n",
    "\n",
    "*The book is available under the [GNU Free Documentation License](http://www.gnu.org/copyleft/fdl.html). If you have comments, corrections or suggestions, please send email to satkumartomer@gmail.com.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Basemap](07.10-Basemap.ipynb) | [Contents](Index.ipynb) | [Subplot](07.12-Subplot.ipynb) >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.11 共享坐标轴\n",
    "\n",
    "常常需要绘制包含相同坐标轴(x或y或都是)的两个或更多的图形。`plt.subplots`提供了一种简单的方法来绘制通用(共享的)的坐标轴。首先，我们将生成具有不同范围的虚拟数据。然后使用`plt.subplots`绘制图形。`plt.subplots`中的其他选项与`plt.subplot`相似。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MRSOJ0VFg0yZgaAggavy7aVPjfRuorqdWs9Oubv+UHZkGjI8Da9ZUGw0KpVNVxQhT7ZZVSFcl3hj7rbHCO0K367pBHfn9VwB8H8AFRHSIiG4lotuI6LbmIdsB7AWwB8AXANxe2Kq4iImo8dQ+PNz4N44skcToKLBvH1CvN/615cQAdQS4ZUu2dqOpuNtvj0/N6UZaZUemwJlIbFZRmqeMaFCohKo2zzTRbpmpOhMqyqzX7Fu1+yipjoyZb2TmZczcw8znM/OXmPlBZn6w+Tkz84eY+W3MvJKZzVQCnj8/akjj3/37G04tDtuRRF5MRIBxqbgHHohPzelGWmVHpkD6fGFXV3J6UebVvKUqmbiJdstO1RVdx5XlmttiPk1HEWLjpZQRpyn8wkq/dlPbJUnhVUsG4qT8LqsRVYrR6Kuvr9Vel68rBTgiv69ayearapHuIS0ZvEvoXrNKJamjjLSN7n3j3jYuSanDKENDjVTU4GAjHWYzkrBNEHGFo5Xe3jMRUlfXmag0CaJGunR8vBH9qPon7XNbZPl+w9ef9LtDQ430sMO4sEN0XBHbotuTtDvB9i6q6vVVbbVikq57u8AxQnMCoX53vQKLQjZ4u42L7hxJMHjZnOMqM42VJtDQTZsGxyXNAVapGIybl1MRFX9UqbJsAzpNyVaUcMotjnapnqGaNzt3/rklW5If9xyZzoCdJEgw5XzKHuzTBmkdB6Ar1Khy/Vh0Xq5WSz4+3C9VqSzbhDKVbL6LB4Dk6vXtVOJrw9UbYivwv3byNW++N/ccWdqAnSRZN+l8yh7s0wbpOGHGunX5hBpVRzbhaHHLluTvO9wvVags2wjbqsHAedG9hNWPrPZbPAC1gyeQF4V0dRldOYpzzjqn5f2TsydzRetVPMS458iCAbuvr/WzNMn6HXeYcz46i7J1pPC66AzS0XTh5z8/92dAzwaTkU3RCDjt+45ef9kqyzbCpmowmoaLzrn4mMKsarlAFRydPhr7fp61dlUoIN1zZEBjYDp8GBgb0x+0xseBI0fiP8sTaSQN9lmk8LoUHaTjbPrgB4H+/lYnYyqyMRUBZ/m+y1z/5xHhaKj7E92ge6nladjmPlN3fOuO1B2pfVuM20lV5U05bdU87B3fusNqlOaeajEvSWq4PKq2JBXh+vV6yrsy1XQ6asCwClBHtZh2TJqKsCplpIPYVC3GqREDylAlju8ex02P3JR6nI8Kv0C1eODYAQwuGsSGqze0TUoxjClFq0oBGUX33Lr3jf+OLBgskwbxsbF8A6hqIM4qhTfVbhK6Nuk61zhH3tMDnHMOcPRowy5VnxMBW7cmLyfoMGw6suFPDyuVdYB9B5LWPiAyf1PYdKwmzq3ztxCg83fZGY4sbrCN0tfXSFuZRHctlMlIcM0aYPv27NFRFF3nqnM+onjnOTTU+NfTNV82sOnI0p6Cs6wHyjOYpbU/tGiobSOZMvFhHWBSdiCKzt+lv+vIspBW7qi3F9i40Xy7JqXwwFzBhKqYbnQOLjr/tWqV3vosXUGHzrwic3y5sOPHzdTDFLRIm8fQnefIO1GvOn/f/D7w3Vy5wq8dlgIAfqwDjJuH7ZsfI+SCWdGM344saVC0qWgzKYWPCiZUxXSjnDrVELcEjm3LloYTDGzo6wPmzZv7O1mcq67DYwYWLJj73pEj/tXD9Jg4UUJAFnFC3oFSJYrYeK2Fh8iMuFJH0IQz9aWifbRO5MZrN1oXzfjtyFSDYpC+sjkXkyaF1207LarUZWqqkXoMbDh8GNi8Ob8KMmsFjihx0Zqs+bJC+CkYAGrUWGSeVZWYd6C0qYbUReUoXIhiTDlTX5cDlPH30X5zZEmCAhdVdLoiDV3yClviCPfXuecCr70GnDyZ7RztVA+zAC7UWkxDNVHvutowae5o9SOrU+sI2lYmmupXH+bITNMZc2SqtVdA6yJdV3ckTtp0Mw95ryluYXM46gwivCyUUQ9TMIav66aSoq60KKaM1KOplKALka+r+O3IgNYUHxDvsExW/TBJ0qabY2Px6b0FC1rnvwLyXNPttwOrV6c7+dHRM4rEKJJG9B5fB8okR5HmnE2kHtPmv0ymBIvuU9au+O/IoqhqJJqs+mGSpIoecZ+NjTVUgUnRUZZrGh8HHnywNb2pcogqx3vbbVI6qg3wcaBMchRx84eBoxrfPV44WtKJ6HyNdH1Cy5ER0TVE9BMi2kNEd8V8fjMRTRLRzubr982bqklWx6RK7ZW5hUtS2SXVZ0nRURZl4Pr16jm6/fvjo7I4x5tX7CJ4h2ty9jRHMbpy9PQxs9xQBQcOR7VViW60pBPRVRnpuvZd2SLVkRFRDcDnAFwL4CIANxLRRTGHfo2Z39V8fdGwnfqoBvG+vuT6gmHH1d8P3HKLOtVWppNLwkTNxDTHr0oxitPqSFyRs4fRcRQqhwOgULSkG9FliXRNOZ+yvqs4e8t2oKmqRSJ6D4B7mPnfNX/+GAAw838PHXMzgBFm/rBuw9bUV0lKRiBetahTIQRoCDBmZ1srWlRZeqmoElOngkeHVuMwiQ+qRR31nq/KxqTqIwt6FuDEqRMAGou4N167UTtaMt0fJpWJNr+r8M7ZBJrTtz1dPSAinJw9o3DOew0mVYvnATgY+vlQ870ov01ETxHRXxLRcoVRa4logogmJicnNZrOQdqcU1wkobuWK1isrDufVAZx15QlYtRZL1ZkHjEa6cZV489yjioj4Ioo477RfXrPOqfkSmorKVUYODEAmJ6ZznRe0/NfJte92VpAnbZlz6n6qTlODLC/dk/HkcWVaIg+2nwDwDAzvwPA3wLYEnciZt7EzCPMPLJkyZJslmYha+rLhOAjeo6kwdfmwJx1mUHY8avIW40jasuRI3OrkegsFXB12USJlHHf6A6gWRR4LqUhk6qfhMk64KaJSbJi0vnYWkCdtHN2EjYrkOg4skMAwhHW+QCeDx/AzEeY+Y3mj18AcKkZ80pgfLzhUIoSHuyTBl/bA3Oena0Dxx8n9y8io0+LdHUi2bJ36u5QdAfQLBGIC1U1AgKHo0PagBvd++2mR27C8ZPHMa82r0VMUmX1DltqybwOyWYFEp0R/HEAK4joLUQ0D8ANALaFDyCiZaEf3w/gWXMmWiRYPxVX37CnpyEQIUpfnBwVjagK/65ZY389W9rO1kmY3oFZp820Y4pcj6CN7gCaRYHnWm3A0ZWjpyOnJJIG3GhaLXBcR6aPGEmnmXQ+ttSSaQ6pp6sH82pz17naXm6Q6siYeQbAhwF8Gw0H9T+Z+Rki+gQRvb952B8Q0TNEtAvAHwC42ZbBxlCtnwIajuuhhxrVLOr1xuLkaKQSLAAOD/ZBtKUq/Ds7W3w9W1paMmlnax1MKhJ12kw7puj1CFpkGUB1FXgu1gbccPUG9HT1KD9PG3CzptWqrt5hY11g3N8KNWeghhYN4aHfeAibr99c7nIDZq7kdemll3JpjI0xDw0xEzX+DX5uuLHWF5HeOaKfqc6n8xoa0ruO3t65v9fb22pL2jG2iPbRunWttmS1a926xvmquB6DAJhgx++bsafGeOj+IaZ7iIfuH+Kxp4r18dhTY9y7oZdxD06/ejf0FjqvCRv7Ptk3x6bgVbu3lno+uodif1f1Grp/KOeVuo3pvxUVuvdNeziyNCcTN7AXdSrBufv6ijmwLAOzyllG7U3qj6Kozq3q53Xrzhzf19d46doVd06ixjk9wwdHZgOTA16aY9RtS+WM6J6YB9gIQ/cPaTuxok7bVcpyYsz6941/1e+j66ZWrWqk/lQV8HV3Tg4gArZuTU+p6a49i6OvD1i4MPvaL1Wl/MBm25X9k9borV9vfkdo1Xfn4bo2H9aRuU7SuqgNV2/QXn9VZH1V0g7IPV09OOesc3B0+qiVKvouoFrntuada7D9ue3GdxDQvW/8cmRxA2l0cXJAMNhl2SaFqFEz8POfTz82q4MMKLJ4WtVmXx8wPa2/nU1ekhzLgQNqJ1tP3s68heBhRdW/ec5ZMeLIiqNa1EwgDC4a1HZORRcdhxcD16iGWZ497UzbzXFFUT0ERBdFm9pepj23cYmTYqucVCCeODe+lloLQ0ONqOaKK/TWeOVRzemoAJPEHKqSVEA5EvUkBaEpUUZ4eYIKEXp0JEnikSwKyaKCikBAwXczZj4+A76bvSmwXBRVP0cfMMpeZuGXI8viPAYHG4Piq6+mH0uUvAVMnDPLMpj29jbWaKWpANPWmKnk8UePxp/PtEQ9yVmZqPsIpK89k+1hOpYkZWVWhWQVtQ/bgSyK0zKXWfjlyFQDqWovrPXrgVOn9M+bZfGtqrRTXx+wbp3eWqxo9KWzxixOHp81GspbWSTJWQVOtq/vzGfz5+udN0yS85XtYbzElCNIiqRsLf51qTqJCyRJ76OUusxCRxFi45VLfaWjjAur4aKy7TTFoOr4ODl+YE9edWDctaheqvbT+iXOnqLy/PA1R1WIcXL7rFJ5XWWmZ6CDVYumJfhJbZlW06lUip0sq48es+6b66x9x7r3jT+OLLxWq1Y7M7glDZKqQbFWi3c+ZQ6iWdad6a4x03Gqpq5RJY0veu4q18FZpFMdme+OoIhU3zeKPHTYkuTr3jd+pBajAoDZ2bkpLRWqVNiWLfGVK0zN8+igO3+l235aRY4gnagSUWSdT8sjvNHBdJksoVJcK1OVFRerk9iiSG3MqncW98OR5S0cm3VQTDveZNX6pA1ATQ/iNpSAWYU3WRgdbTjvwcFGO+vXd1S1+3bCd0dga+7NRXx+6PDDkRUthKtbOzBpk0rTVetV0d/GjeZ3X7ahBNQV3gDA8ePZ+km2bmkbfHcEtgrvuojXDx06+Ucbr0y5/jLmrtLmZmzYYLOUVJgk0UvedpOEN3Flu3p69MtTZZ3b9AB06BwZc7kljTqdIn1dljAni426940fjixJWGBqUEtzVFkVjS5h60FA5Yh1hCxJAo6salMPcMmRiWMph7L72YQjsm1zVhvby5Exz1Ut2qiGnuaoqpCFm4rYylYC6jiipAhLV9HpkSTfFUdWphw+yYZ2d6RV9LNNhaip7yyrjbr3jR9zZMCZua6hocYwFsZEOaa0RcVlKhoBs/NERZWAWUUuuuKO2dkz1/bBDwL9/Y02jh8H5s1L/33ZXDMzKmXamq+vKWWRb9oC43apolHF7ti2xBomF4XbstEfRxZga8fgNEdVxBnkUTvmVWqqCB4Etm5t/Lx6tZ4teRyqqupJEqdONTYdZT7zb9oO3czF1aMdhmrAmOVZrH5kNehewvCnh3H7/7k91aHkcTpJA3yWAdN1h1eFAtCWWMOkU7Zlo3+OLEs5piwOJMlRBedZvbpx7Nat+orCNEegstGkww7aIGpcQ5pTCtv0gQ9kd6jRvuzr04uwwpw61djqRrVDd4AoGjORNGBws/Dr/mP78cDEA4kOJe9TetIAnxQthh2WD2WjqlAAqspHrVqxqtB5TTplWypWLUdGRNcQ0U+IaA8R3RXz+VlE9LXm5z8kouFCViWhm+LLE0nESfWLpviSIqukc9uqJp+Wlo3apNouJc2hhvvy8GFg8+Yzjk0VYanaCDvGOGxU+m9T4gYSHaJP4Hmf0vNUsJ/l2TkO645v3VF62i4rVSw7GF05ijXvXDOn9iGDsWXXlkJO3qRTtrWcIXU/MiKqAfgpgF8FcAjA4wBuZOYfh465HcA7mPk2IroBwG8y8+8lnbfQvkpJ670CTG3KWPQ8SZthDg6qz71hg3oTyyxry3T2TQvv76W7z1qRzS11NyWNayOpPx3eo8yl/cjGd49jzdfXYJZnM/0egVC/u9HHSXuDBceo2lbtBRbs8ZWXtLbLJti3zPRmk0kU2TRURdH924qge990a5zrMgB7mHlv88RfBXA9gB+HjrkewD3N//8lgM8SNeR+mazWZXQ0fTA3lZoreh6VswqqVqjOHVxf0V2fdewMR3m611VE5BK9tnPPBV57DTh58swxKiFNUn8KWgSDj2qnYxXhJ3DVRpZpT+lB26oBPqtNWdoum9GVo6UvnLYxN5f2nbmATmrxPAAHQz8far4XewwzzwA4BqAvcgyIaC0RTRDRxOTkZD6LdTGVmit6nqRUaNq5s1QlUZFmZ9Rh6FzxmCTrAAAfmElEQVRXX1/xiiNJqcckIU3Z6lEHsHHfhFM8gHorjoBoWqxI6kxVly+adqpRfAq6b36f19VCbGJrbq7qWoqppOnzAfx7AF8M/bwawJ9HjnkGwPmhn38GoC/pvNYrFJhaO2XiPKr1YGWs78q6mDxtexkXFiKXVRHFIHBkHZmKuK05sm7nUebi2U5Yi5YHF9YJmkT3vtFxZO8B8O3Qzx8D8LHIMd8G8J7m/7sBHEZz/k31KqXUjskFxbYGzjIG5axtJO055oHTcBHXHZmriMPKTjv1me59oyP26EZD7HE1gH9GQ+zxH5j5mdAxHwKwks+IPX6LmX836bwmJq0FwRdcEnsIgi8YE3sw8wwRfRiNqKsGYDMzP0NEn0DDW24D8CUAW4loD4CjAG4oZr4gCIIg6JEakVlrmGgSQJrWth+NNGXVuGIH4I4tYkcrSbYMMfOSog1o3De+9EeZuGIH4I4trtgBGLhvKnNkOhDRhIl0TLvYAbhji9jRigu2uGBDgCu2uGIH4I4trtgBmLHFvxJVgiAIghBCHJkgCILgNa47sk1VG9DEFTsAd2wRO1pxwRYXbAhwxRZX7ADcscUVOwADtjg9RyYIgiAIabgekQmCIAhCIuLIBEEQBK8RRyYIgiB4jTgyQRAEwWvEkQmCIAheI45MEARB8BpxZIIgCILXiCMTBEEQvEYcmSAIguA14sgEQRAErxFHJgiCIHiNODJBEATBa7qrari/v5+Hh4eral4QSmXHjh2HTewQLfeN0Eno3jepjoyIlgN4GMAAgDqATcy8MXIMAdgIYBWAKQA3M/MTSecdHh7GxMREWvOC0BYQ0X4T55H7RugkdO8bnYhsBsAfMfMTRPQmADuI6G+Y+cehY64FsKL5+iUADzT/FQRBEASrpM6RMfMLQXTFzK8BeBbAeZHDrgfwMDf4AYDFRLTMuLWCIAiCECGT2IOIhgG8G8APIx+dB+Bg6OdDaHV2IKK1RDRBRBOTk5PZLBWEDkXuG0FIRtuREdFCAH8F4E5mfjX6ccyvtGw9zcybmHmEmUeWLCk87y0IHYHcN4KQjJYjI6IeNJzYODM/EnPIIQDLQz+fD+D54uYJgiAIQjKpjqypSPwSgGeZ+c8Uh20D8AFqcDmAY8z8gkE7BUEQBCEWHdXiFQBWA9hNRDub7/0XAIMAwMwPAtiOhvR+Dxry+w+aN1UQBEEQWkl1ZMz8D4ifAwsfwwA+ZMooQRAEQdBFSlQJgiAIXiOOTBAEQfAacWSCIAiC14gjEwRBELxGHJkgCILgNeLIBEEQBK8RRyYIgiB4jTgyQRAEwWvEkQmCIAheI45MEARB8BpxZIIgCILXiCMTBEEQvEYcmSAIguA14sgEQRAErxFHJgiCIHiNODJBEATBa8SRCYIgCF6T6siIaDMRvUxETys+v4qIjhHRzubr4+bNFARBEIR4ujWO+TKAzwJ4OOGYv2fmXzdikSAIgiBkIDUiY+bvAThagi2CILQpPMs48KkD+If+f8CBPz0AnuWqTRLaCFNzZO8hol1E9C0iulh1EBGtJaIJIpqYnJw01LQgtDc+3jdhx/Wzu36GiUsnsO+efZg5MoN9d+/Djl/cgannpqo2U2gTiDn9yYiIhgF8k5nfHvPZOQDqzHyciFYB2MjMK9LOOTIywhMTE9ktFgQPIaIdzDxS9Dw+3DdTz03hmd99BtPPTaN+oh5/UBfQ09eDK16+olzjBK/QvW8KR2TM/CozH2/+fzuAHiLqL3peQRD85MkrnsSJp06onRgA1IEFb19QnlFCW1PYkRHRABFR8/+XNc95pOh5BUHwkwUXLwASfBgA1BbWMHDLgNb5qpxfk7k9P9CR338FwPcBXEBEh4joViK6jYhuax7yOwCeJqJdAD4D4AbWyVcKguANWQb0gVsHUFtYSzwfdRP6r0tP3Ew9N4WJkWrm17K2XcTpicMshtYcmQ18yPULgilcnyPjWcbB+w/iwB8fwODHBrH8zuWgGgFonfPqWtCF3l/oxUVfuwi9K3pbzrH/v+0HTzPqr58Jy7oXd+PyfZeje5HOip8zPPbmx3DqyKm5EV5J82tZ2tbtoziK/G67U9ocmSAIfpMWeUTnvOon6ji+6zievOLJ2HPM/nwWqAEL370Ql/30MlzFV+HKn1+Z2YkBijRlSfNrWdrW6SMVRX5XaCCOTBA6nLSBVGdAtzUYx6UpVfNrptNzWdou4nBNOutOTVGKIxOEDidtINUZ0G1FTv3X9YO6ae5pZ+rYc8eeOQO1jbm0uLZVc3tZnF5A4HRenXgVdNbcdrKIYQKqnE+sGnFkgtDhqMQZPQM94FnWGtDzDOQ6dC/qxpU/vxJX8VW47KeXYcG7FoBqhJmjcwdqGxFhuO3gpUqRZnF6wFynUz9eB7/B2r+ropNTlNmT1oIgtBX91/Vjz3/c0/L+kUePYMcv7sBFX7sIV/78ysznyDMYJ/HkFU/OEV+EB+oFFy/AK3/3ytxfKHGtWuD0kggLauqv11GfrmuLWJLEOAFV90GViCMThA4nGISjKr361BlHkaYQ1BnIi5I0UA/cMoDXJl7D7PHZOR8HUWV00C+blmonXdBOxUZ/d9/d+/Dy+MstqsaBW1v7wERU7AOSWhQEAUD+ea6yBAZJ6cu41B5wJqqsep6opdpJzIJxldPRTRlmTW+2E+LIBEEAkG+eq0yBQdJAHUSEPUt65oxq4aiySnSqnaicju4DRpY5vXaj/a9QEAQt8sxzJc1bmV6wrJO+dHWeSJX2W/HACgzclJz600kZ6syhtTMSkQmCACDfE31atFD2uiZb6smi9K3qA8/kUyampQw7WXYfIBGZIAi5SYoWdEUKJilDPanL6ZJdG/aj1lsDmv41XIJKJ+2XFomWGRW7ijgyQRByk+Q4frjih6UPsGWoJ3WIOvHZV844etP94Go6tUzEkQmCkJskx9EpA2zc/FQ0SmrBYD90suw+QObIBEGwgqvzVTrozu2p5qfOfsvZiSpFk/3QybL7AInIBEGwgkvzVVnIMrenmp+qLaihtrDWskA7oEg/xEWALqRTq0QcmSC0Ga5IsV2Zr8pKFvGEKn268F0LcWL3iTlv592TLUwVAhofkNSiILQRvkuxXdiGJEuFE1X6dNnaZVYWJ2cpDOxCX5ZFqiMjos1E9DIRPa34nIjoM0S0h4ieIqJLzJspCIIOT17xJE7s8rMCuitOOMvcns35qThHpOtkXenLstCJyL4M4JqEz68FsKL5WgvggeJmCTYYGACIWl8DA9mOKcsWV3HV9qnnpjA7NQtEH7w9UQq6sg1JFudkqyyUyhGd+2vnajlZV/qyLFJ7m5m/R0TDCYdcD+BhZmYAPyCixUS0jJlfMGSjdQYGgJdean1/6VLgxRfLt8cWcdcYfV/nmLJscRVXbX/yiidRn4qXyvmgFHRFru/C3J5qnu71A69rOVlX+rIsTMyRnQfgYOjnQ833vEFnYHL1KVwQAhZcvKA1GgOAbjivFATKk+vzLGP/ffvx3QXfxfcWfA8H/uTM/JEr80qqFOLCdyzUigB9XvqQBxOOLE4OFfvtE9FaIpogoonJyUkDTSdjwvkE5yjyFG7SDnGknYfufaMavC586EKnKqCrHEkZ66GmnpvCj1b+CP901z+Bpxj1qTr23rUXj698HEf/5qgz80pFHVGnrS2jRkYw5aBGavGbzPz2mM/+AsDfMfNXmj//BMBVaanFkZERnpiYyGOzFqp0YUD4ssmAMjkpDZl2fp0UZtI54r7CtOvPg8afijZZr8cl8thORDuYeaRo20n3zcyxGfxg+AeYeWXm9Htxku+y5fnh9pb9/jIcfvQwpn8yfeZxtwvovaAXb3/07dYl5I+9+TGcmjyl/wsJuzbbRPe7bHd07xsTPbINwIeJ6KsAfgnAMRfmx8qeryjSng1bq56vEcpHZ26n7HVI0fYOfvJg60F1YOrZKTzx3icw+NFBqw42du4oiRLmleIeLFyYp/MJHfn9VwB8H8AFRHSIiG4lotuI6LbmIdsB7AWwB8AXANxuy9ik9Fr0M8EsS5dmOz4tFao6X9Z2qsBn28tWs7XsjJxA/fW69bTewK0DoLP0B4i4dJ7JebROk8nbItWRMfONzLyMmXuY+Xxm/hIzP8jMDzY/Z2b+EDO/jZlXMrO1fGHSPFWRCMTngQmIdxpJMM996RybVb2ZNqf44outduRpR5csD0Fp849l226SLIt9rbWnoH6ibt3B9l/Xj66z9aUB0XmlqOPZ+7G9+O5Z38XP7vpZLoeW98HCFVGKK3RsZY/ooO/rwAR0bhoxiwPK8xDUbv3Ks4yegZ6W922q2eJEC7HUUMr6t+5F3fjlV355jurvwq0Xxotktl7YogiMLjjHKQCzwMFPHswVSeV5sJAorpWOdWRhkgYsE1GZzXP4EjXaoFMckAmCwe/wtsMtn9lUs8Wp57oXd+PKV+ZKyC/8crwzKeJgdaMWXYWfcsF5kzwRZB51YqctdtbBefmLDfVdFqJRWdLi6bRz5PldlR1hss4JBscHasmlS/PbJfhB7P5YJSjydEULpivlZxG16NqYtOAcgFYEGRV2LPvgMuzpznbdnbbYWQfnHZkpJ8ZsRgRSJN1YRaoy6brD81VCPFGn7ys2Bj9TMn6eZTz/heeBGvDWT73ViFoxSwV7XdIUj2mRVNS57v3YXuz96F4s/8/L8dYNb9W+5riNNAHg2GPHcOBPDmD5H1az20GVtEVqcenS5OhBIovyabdU6Esv+bsQ3cbcmKl5GlvzPTZELWnzfWmRVIuCM+f8WlwqFAD4JJ9e3N1p82VeO7KwKEOlJPNJtJEXF52Ga8o+033hyzycrbkxU/M0Rc6TNAdmo0RT2nxfWrHgJAVnlr4LUqE9S1ofToI1eZ02X+akIwur0Uxie8CvqoxU1GkE1xNEESYxKWMvStL3Fm07zYHqLEXwkcBR8FTo4rqAniU9haq0m4p4VOepnVNLFGmkRXI2SjQVrXSfGNHl7TvVZx02X+akI7P1tKsbJeQdkF1R0dloT6fmpO715+3f6O/pXKcvkZMtbK0bMxXxxA7uXcAbh95ITDWmRXK2tlcpgiolCOj3XTgK7VnWE7u4m86mti0OrMJJR6aDzbSZKw7JNln60OS15+3fojakRXA+zXvpUsThJKXuTEU8sYN7HeA3Gm2pUo1lL+w2QeBcr3zlSnQvnutQdfouGoUefvQw+FRrtNp1VlfbFgdW4bxqMYqv6Z9ois+0Ck53mYKv/WeCoL+TVJyqpQiuo1IQJsnak1SHafJ1U7UAo+fZ+b6dWurKOOWeL9uU5O27qBKTp/h0mrjsosau4V1E1i7bmZS1UaUr+PId6fajS+rLpPkiVYrt5MsnE+eYqlp0qxtBdto2JYCfUWhZeBeR2Uz7Vb34Okq77VztUt/mxcWINs+aqdjf2XkcE++eQNfZXehe3N263UkJg6buwuhOrA7vcxRqGycjsqrk5DbnYOJIiyxNO22XoogkVFF3XnyJBvOS50k99ncYqE/XMXNkBm8ceqNldChj0HRRpOEKnRiF6uLkX4fJckwmSXMEL75Y3D4bUYuJKCKpjFXW604riVXEUduM2F19EMjzpK6qDnF6/uWN1j8anwbNsjcPLYNOjEJ1cTIic5FOWFidhMkFzkXOpVr0nsWWrA6p6sXcaeR5Uk+SgodZ/L7FViMjG9uRSHX4zqOtHJmu+CNP6kpXYFLVU7uL1T1cxVWHlJc86bjuRd245EeXYMG7FqBrQfwwkCWVmMch2XI4sUKV5vxfEYcpe4C5i3eOTHdgTkolmUrfxZ0nqVSW7maWeai6JJTu9+KK2lTX3qVL20cpGyVt92bdVGJeh2RLGamc/ztRz+0wJcpzGy1HRkTXENFPiGgPEd0V8/nNRDRJRDubr983b2qD6IDdzvgUZaU5cBXRYrxlzYHqPnC8+GL7LpBX1f4L0om6qcS8DqnMqiNzmsjhMMtcjiCRX3ZSHRkR1QB8DsC1AC4CcCMRXRRz6NeY+V3N1xcN21kaZURPgJ6TqjrKcg0dB1628/c5KjNVZiqLQ4qWWDJd2BfQnP/L6DBV19h7ca9RpyORXz50IrLLAOxh5r3MfBLAVwFcb9es9idwUtEBNohQdAZI11NeRYs/R/snHL2prjGL8zfRf1m+L9cwJefWdYhxJZZmp+eqJk0oI8NzhouvWhx/UA2ZHGbcNXb1duH1va8bdTomIr9OjOh0JEjnATgY+vkQgF+KOe63iehXAPwUwH9i5oMxx7Qd4UE6z0LlpLQVUfI5XU95mbDD5jWaPLcrfZ4FU3Ju3UXMVZRYGrh1AK8+/mrLPGDWeoRx11ifrjfW2xnavJNnGbU31QotRM+yM3Y7oRORxT1PR138NwAMM/M7APwtgC2xJyJaS0QTRDQxOTmZzVIFeVJJWX/HhMAkLz4OkK5QdsRq6/w27huT6KomTc6J6UYd/df1o6tn7jDXvbgb733+vZmWEsRd4+J/vdjY9QTR6hv//EbLZ1nSrVWVFqsaHUd2CMDy0M/nA3g+fAAzH2Hm4Bv4AoBL407EzJuYeYSZR5YsWZLH3hbyzCNl/Z2sApMse3blJW2wLGPwrjq1Wda2Olnn10w/fNi4b6og65ycylllmUeyWSnE5Oadp/eNK7gQvVPrMeo4sscBrCCitxDRPAA3ANgWPoCIloV+fD+AZ82Z6B959uwy1UbS8XFOR/XScUa2U5tpjr6saLVTxTWmyTInF+esJkYmsOeje/CjC36EE7vsRR1Zoj1TJaOSFKRFN+/shHqMqb3DzDNE9GEA3wZQA7CZmZ8hok8AmGDmbQD+gIjeD2AGwFEAN1u02WnKiEbKaKOKlCZztSXIkvB1exeXyDInF1fU+MTOEzix80T8LxiKOrLMMZksGWWqILDufGW7obWOjJm3M/MvMPPbmHlD872PN50YmPljzHwxM7+Tmd/HzP+Y16Cy0lW22iljsPNlQHVxvVuYLHOiKpWpb/iiaFNFKCpMRR1VzTGZiu46teiyc5U90lR8phxakbRYmYOZzwNn3FykTbLOPxadX/UNn9YopS1qbqEGvL7v9cIOuqo5pjIckC8PMXlwzpGlUVY0khSx2Zoz6YTFzzYXLJe9KaZPlVeA4tFGmQOhzqLm2sIaLthyAZZ/ZDlmj89i/3/dX9hBt+sck08PMXnwzpGVRdlrtEwNfl0lfqN5BnLT1Up0U8I2Hg58q7xSJNooeyCMRihXvnIluhdHohMCDn3qEA7edxCYBfhkw7EWSQe2655f7S7Lb+/EqcOkpaaSdodOYjayvVTeXa91HKvJATvNzrR9z3yZN6ySIoKCPLtQmyROWPHYmx/DiWfMij/i2mmHvc0WXLwAr/zdK3PfbCNZvkRkOTERQSWlL/NEhEkihayvsqOKpOtyOcrxiSLRhiqam/n5TGXzLUmCEFPpwHZJybVryjTAOUdW1vxCUjtZhQJ52zaZvpTBXkijiKBAJb7gGa5scE8ShJhKB+ZNybkmrGjXlGmAc6nFYDDOm1rL2k6ArfVLLqvbkvq4nZyiq+ILn4hbnxRQdpoxyabuxd24fN/lxtR+eVJyLtY7NLnmzUWcc2QBeQdS3TmhvIN1Ow2KrhcdDihSPT8vneLkdQkPhDvft9OJ+ZYyBuc884pVzyd2Is6lFouiOwhnGazT5o58k2EL6fji5G2RlBpr9/mWMHlScp1a77BK2s6RVYENGXanOcGs1xv0ser3urrc3qutLPLM1aQJHNptviWpj7LOK/Iso2egp+X9dnX0riCOrEKSBu8sT/42dz/WKSpctNxX3iUCgPohoq5Qs3VKRAXkV9zFChyePI6Jd0+AZzl1cHdN6JCESVVicK7D2w63fFaVo/fpuyiCOLImVaQHs24PE4cttWKacwlX0y+ahusk51ImeRV3Kll7fbqeOsj7Jlc3uVD49FYsU6GbublxaBX1Dn37LoogjqxJ3vRglXtyFXWySc5bnIv/5J2rUcra60gd5E1XkLAdUZicz1Kdq3ZOrZKISBVZP/HeJ0qzoSy8dmRxTkQXU5FWmaIAU3NwQb9FbVy61I/1aGXMH7aDgGfggwOgs+beFDpzNYl1DlMGeZOOoYyIwqRwJfYBoAt449AblUREysj69XrbRWVeODJV1JNWDSL8ig5Apqvp+4RPiryqqo74VkcxytRzUzjwqQOtOw4TUudqgjmwC7deGDvIL715aSmKxjLqA5oUrsQ+ANRx+jvIa3/eqHTg1vg+r5+ot02NxQBn15GFMTHApm0PE6ZT1wq1E6r0qE8RVRFOr2UK0wV0nd2lPVcTuwi6Wah3em/8Yl+TGzuWUR/Q5Fq06LlMrLcrsri6/7r+xlbIkfqr4PZbCuBFRFY2VUUmSYOsjwNwYHPaPKKNNJ7vEVVRTKT44tSJXWd34cQz6ijJ5L5apterxUU2Nufgitgf2PWjC36EE7vyRaXdi7px4Zfjo+p2Wwogjswhkgr8ljUAZ51rjBLMs4VtTktldrrTsYGtRctlLvY1mfaLm297fOXjeHzl49bm4PLaH7YVjMYrTIb+brc1fyq0HpOI6BoAG9EIVL/IzH8c+fwsAA8DuBTAEQC/x8z7zJrqJp2SwmL2s2yTjzabwGSKL0yRrWCyYjLtF1c2aurZuQ7LdCmpvPZHbY2Spb/bvcZiQKojI6IagM8B+FUAhwA8TkTbmPnHocNuBfBzZv6XRHQDgE8C+D0bButQphPxcTDMK6/38Vp9EraYxNYAZstB2iZ2vi0OB0pJpdnqQ3+XjU5EdhmAPcy8FwCI6KsArgcQdmTXA7in+f+/BPBZIiJmM7Xfswy8qhZlbdQZVA7J1g4AQvvg6xN+XCRJZxFAAL9+ZtBwYf5IFfWueGAFBm5qr7ktU+jMkZ0H4GDo50PN92KPYeYZAMcA9EVPRERriWiCiCYmJye1jTRRASNuHqYd1goJ7U/e+0Y4Q9xcUdfZXeg6a+4Q6EK00ynzWibRicjintOj7kTnGDDzJgCbAGBkZKTyol8+psp8pFPmEW3h2n3jIz5Fkj7Z6go6EdkhAMtDP58P4HnVMUTUDWARgKMmDIxDIik72OpXUSUKgmATHUf2OIAVRPQWIpoH4AYA2yLHbAOwpvn/3wHwf03Nj8UhA6Md2rFf5aFHENqf1NQiM88Q0YcBfBsN+f1mZn6GiD4BYIKZtwH4EoCtRLQHjUjsBptGC4IuPjthQRD0IIuBU3LDRJMA9qcc1g+gdXOf8nHFDsAdW8SOVpJsGWLmJUUb0LhvfOmPMnHFDsAdW1yxAzBw31TmyHQgoglmHhE7zuCKLWJHKy7Y4oINAa7Y4oodgDu2uGIHYMYWKVElCIIgeI04MkEQBMFrXHdkm6o2oIkrdgDu2CJ2tOKCLS7YEOCKLa7YAbhjiyt2AAZscXqOTBAEQRDScD0iEwRBEIRExJEJgiAIXuOkIyOia4joJ0S0h4juKrnt5UT0/4joWSJ6hojuaL5/DxH9MxHtbL5WlWDLPiLa3WxvovneuUT0N0T0XPPff2HZhgtC17yTiF4lojvL6g8i2kxELxPR06H3YvuAGnym+XfzFBFdYtmOPyGif2y29XUiWtx8f5iIpkN986ApO1JsrOS+cemeabYr902n3TfM7NQLjeohPwPwVgDzAOwCcFGJ7S8DcEnz/28C8FMAF6GxTc1/Lrkv9gHoj7x3H4C7mv+/C8AnS/5uXgQwVFZ/APgVAJcAeDqtDwCsAvAtNIpYXw7gh5bt+LcAupv//2TIjuHwcSV+N5XcNy7dM00b5L7psPvGxYjs9P5nzHwSQLD/WSkw8wvM/ETz/68BeBat29ZUyfUAtjT/vwXAb5TY9tUAfsbMaRVZjMHM30NrAWpVH1wP4GFu8AMAi4lomS07mPmvubFtEQD8AI2C2lVR2X3jwT0DyH0DtPF946Ij09n/rBSIaBjAuwH8sPnWh5vh8GbbqYkmDOCviWgHEa1tvreUmV8AGgMIgDeXYEfADQC+Evq57P4IUPVBlX87t6DxVBvwFiJ6koi+S0S/XEL7Ttw3DtwzgNw3Ktr2vnHRkWntbWbdCKKFAP4KwJ3M/CqABwC8DcC7ALwA4E9LMOMKZr4EwLUAPkREv1JCm7FQY+eD9wP4X823quiPNCr52yGi9QBmAIw333oBwCAzvxvAHwL4H0R0jm0zYt4r9b5x5J4B5L7Jivf3jYuOTGf/M6sQUQ8aN+Q4Mz8CAMz8EjPPMnMdwBfQSOVYhZmfb/77MoCvN9t8KQj7m/++bNuOJtcCeIKZX2raVHp/hFD1Qel/O0S0BsCvAxjlZqKfmd9g5iPN/+9AY+7qF2zagYrvG1fumWa7ct/E07b3jYuOTGf/M2sQEaGxLc2zzPxnoffDOePfBPB09HcN27GAiN4U/B+NCdKnMXfvtzUAHrVpR4gbEUqPlN0fEVR9sA3AB5oqrMsBHAtSKTYgomsAfBTA+5l5KvT+EiKqNf//VgArAOy1ZUeTyu4bV+6ZZpty36hp3/vGlDrF5AsNFc1P0fDI60tu+0o0wuqnAOxsvlYB2Apgd/P9bQCWWbbjrWgoz3YBeCboBwB9AL4D4Lnmv+eW0Ce9AI4AWBR6r5T+QGMQeAHAKTSeHG9V9QEaKZLPNf9udgMYsWzHHjTmFoK/kwebx/528zvbBeAJANeV9LdbyX3jyj3TtEXuG/Xfa9veN1KiShAEQfAaF1OLgiAIgqCNODJBEATBa8SRCYIgCF4jjkwQBEHwGnFkgiAIgteIIxMEQRC8RhyZIAiC4DX/HzMHJxw67WIgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2701863e240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x1 = range(100)\n",
    "x2 = range(125)\n",
    "\n",
    "y1 = np.random.rand(100)\n",
    "y2 = 2.0*np.random.rand(125)\n",
    "y3 = np.random.rand(125)\n",
    "y4 = 1.5*np.random.rand(100)\n",
    "\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex=True, sharey=True)\n",
    "ax1.plot(x1,y1,'ro')\n",
    "ax2.plot(x2,y2,'go')\n",
    "ax3.plot(x2,y3,'bs')\n",
    "ax4.plot(x1,y4,'mp')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>图7.14:有通用的(共享的)坐标轴图</center>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
